updates

utils/onnx2coreml.py

@@ -68,13 +68,14 @@ def main():
         spec.neuralNetwork.preprocessing[0].featureName = '0'
 
         yolov3_model.save(name + '.mlmodel')
+        # yolov3_model.visualize_spec()
         print(spec.description)
 
         # 2.5. Try to Predict:
         from PIL import Image
         img = Image.open('../yolov3/data/samples/zidane_416.jpg')
-        out = yolov3_model.predict({'0': img})
-        print(out['141'].shape, out['143'].shape)
+        out = yolov3_model.predict({'0': img}, useCPUOnly=True)
+        print(out['148'].shape, out['150'].shape)
 
         # 3. Create NMS protobuf
         import numpy as np
@@ -106,15 +107,15 @@ def main():
             del ma_type.shape[:]
 
         nms = nms_spec.nonMaximumSuppression
-        nms.confidenceInputFeatureName = '141'  # 1x507x80
-        nms.coordinatesInputFeatureName = '143'  # 1x507x4
+        nms.confidenceInputFeatureName = '148'  # 1x507x80
+        nms.coordinatesInputFeatureName = '150'  # 1x507x4
         nms.confidenceOutputFeatureName = 'confidence'
         nms.coordinatesOutputFeatureName = 'coordinates'
         nms.iouThresholdInputFeatureName = 'iouThreshold'
         nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
 
         nms.iouThreshold = 0.6
-        nms.confidenceThreshold = 0.9
+        nms.confidenceThreshold = 0.3
         nms.pickTop.perClass = True
 
         labels = np.loadtxt('../yolov3/data/coco.names', dtype=str, delimiter='\n')